statsmodels.regression.linear_model.RegressionResults.scale static RegressionResults.scale() [source]

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.median ExpTransf_gen.median(*args, **kwds) Median of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional Location parameter, Default is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

statsmodels.robust.scale.HuberScale class statsmodels.robust.scale.HuberScale(d=2.5, tol=1e-08, maxiter=30) [source] Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations

statsmodels.robust.scale.hubers_scale statsmodels.robust.scale.hubers_scale = Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations. The default is 30. Notes Huber?s s

statsmodels.sandbox.distributions.extras.SkewNorm_gen.cdf SkewNorm_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: cdf : ndarray Cumulative distribution func

statsmodels.regression.linear_model.RegressionResults.eigenvals static RegressionResults.eigenvals() [source] Return eigenvalues sorted in decreasing order.

statsmodels.emplike.descriptive.DescStatUV.ci_skew DescStatUV.ci_skew(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence interval for skewness. Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value of skewness the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of skewness the lower limit can be. Default is .99 confidence level assuming normality. Returns:

statsmodels.emplike.descriptive.DescStatUV.test_skew DescStatUV.test_skew(skew0, return_weights=False) [source] Returns -2 x log-likelihood and p-value for the hypothesized skewness. Parameters: skew0 : float Skewness value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple The log-likelihood ratio and p_value of skew0

statsmodels.emplike.descriptive.DescStatUV.test_joint_skew_kurt DescStatUV.test_joint_skew_kurt(skew0, kurt0, return_weights=False) [source] Returns - 2 x log-likelihood and the p-value for the joint hypothesis test for skewness and kurtosis Parameters: skew0 : float Skewness value to be tested kurt0 : float Kurtosis value to be tested return_weights : bool If True, function also returns the weights that maximize the likelihood ratio. Default is False. Returns: test_results : tuple

statsmodels.emplike.descriptive.DescStatMV.ci_corr DescStatMV.ci_corr(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence intervals for the correlation coefficient Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value the upper confidence limit can be. Default is 99% confidence limit assuming normality. lower_bound : float Minimum value the lower condidence limit can be. Default is 99% confidence limit assuming normal